Skip to main content

Advertisement

SpringerLink
Log in

Integrated methodology for urban flood inundation modeling: a case study of Ichinomiya River Basin, Japan

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

Abstract

Important cities in the low-lying region of the Ichinomiya River basin have experienced severe inundation in the past as a result of heavy downpour. Therefore, modeling the potential impact of floods within the river basin is extremely important for future flood disaster risk reduction and adaptation plans. Flood risk analysis requires adequate understanding of the potential hazard’s characteristics and their potential impacts on the environment. Rainfall of the study area (from 1996 to 2014) was characterized using Gumbel distribution to determine the Intensity, duration frequency (IDF) of rainfalls. An integrated grid-based approach (Hydrodynamic (Flo 2D) and height above nearest drainage (HAND) models) to predict the potential flood intensity and hazard zones in Ichinomiya River basin, Chiba prefecture, Japan. Rainfall analysis revealed a decrease in the intensity of rainfall within the selected years. However, there is possibility of experiencing extreme events in the future due to climate change. Results of Flo 2D and HAND models agreed in terms of the flood extents and areas affected by flood in the area, with the potential flood hazard zone advance towards the north east and within the depressed/lowland areas. Future extreme events showed potential high risk of floods, especially in the downstream parts of the basin. The outputs of the flood models can serve as tools for decision making in future flood disaster risk reduction plans.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ahmed I, Gerlach RM (2012) Distributed flow guided dydraulic modeling of a desert river system for flood control. In: Proceedings river flow 2012, San Jose, Costa Rica, vol 1, Taylor and Francis Group, LLc, pp 1007–1015

  • Bates PD, De Roo APJ (2000) A simple raster-based model for flood inundation simulation. J Hydrol 236(1–2):54–77

    Article  Google Scholar 

  • Bates PD, Pappenberger F, Romanowicz RJ (2014) Uncertainty in flood inundation modelling. Appl Uncertain Anal Flood Risk Manag Chapter 10:232–269. https://doi.org/10.1142/9781848162716_0010

    Article  Google Scholar 

  • Bhatt CM, Srinivasa RG (2008) HAND (height above nearest drainage) tool and satellite-based geospatial analysis of Hyderabad (India) urban floods, September 2016. Arabian J Geosci 11:600. https://doi.org/10.1007/s12517-018-3952-1

    Article  Google Scholar 

  • Chang N, Guo D-H (2006) Urban flash flood monitoring, mapping and forecasting via a tailored sensor network system. In: Proceedings of the 2006 IEEE international conference on networking, sensing and control 2006, issue 23–25, pp 757–761, April 2006

  • Chen H, Ito Y, Sawamukai M (2016) Spatial and temporal changes in flood hazard potential at coastal lowland area: a case study in the Kujukuri Plain, Japan. Nat Hazards 84:1513–1527. https://doi.org/10.1007/s11069-016-2495-6

    Article  Google Scholar 

  • Chow VT (1959) Open-channel hydraulics. Blackburn Press, Calwell

    Google Scholar 

  • CRED (2015) The human cost of weather-related disasters 1995–2015

  • Cuartas LA, Nobre AD, Hodnett M et al (2012) Distributed hydrological modeling of a micro-scale rainforest watershed in Amazonia: model evaluation and advances in calibration using the new HAND terrain model. J Hydrol 404:13–29

    Google Scholar 

  • Dutta D (2011) An integrated tool for assessment of flood vulnerability of coastal cities to sea-level rise and potential socio-economic impacts: a case study in Bangkok, Thailand. Hydrol Sci J 56(5):805–823. https://doi.org/10.1080/02626667.2011.585611

    Article  Google Scholar 

  • Dutta D, Herath S (2001) GIS based flood loss estimation modeling in Japan. In: Proceedings of the US-Japan 1st workshop on comparative study on urban disaster management, Port Island, Kobe, Japan, February 2001

  • Dutta D, Nakayama K (2009) Effects of spatial grid resolution on river flow and surface inundation simulation by physically based distributed modelling approach. Hydrol Processes 23(4):534–545

    Article  Google Scholar 

  • Dutta D, Herath S, Musiake K (2003) A mathematical model for flood loss estimation. J Hydrol 277(1–2):24–49

    Article  Google Scholar 

  • Dutta D, Herath S, Musiake K (2006) An application of a flood risk analysis system for impact analysis of a flood control plan in a river basin. Hydrol Process 20:1365–1384

    Article  Google Scholar 

  • Flo-2D (2009) Flo-2D Reference manual. In: FLO-2D Software, I., ed.: Nutrioso.

  • Gharari S, Hrachowitz M, Fenicia F, Savenije HHG (2011) Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment. Hydrol Earth Syst Sci 15(3275–3291):2011. https://doi.org/10.5194/hess-15-3275-2011

    Article  Google Scholar 

  • Herath S, Wang Y (2009) Incorporating Wind Damage in Potential Flood Loss Estimation. Global Environmental Research, London, pp 151–159

    Google Scholar 

  • Ito Y, Chen H, Sawamukai M, Su T, Tokunaga T (2015) An analysis on the relationship between land subsidence and floods at the Kujukuri Plain in Chiba Prefecture, Japan. In: Proceedings of the international association of hydrological sciences. https://doi.org/10.5194/piahs-372-163-2015

  • JAXA (2014) ALOS research project-landuse landcover. Japan Aerospace Exploration Agency, Tokyo

    Google Scholar 

  • Komolafe AA, Srikantha H, Avtar R (2018a) Sensitivity of flood damage estimation to spatial resolution. J Flood Risk Manag. https://doi.org/10.1111/jfr1113.12224

    Article  Google Scholar 

  • Komolafe AA, Herath S, Avtar R (2018b) Methodology to assess potential flood damages in urban areas under the influence of climate change. Nat Hazards Rev 19(2):05018001

    Article  Google Scholar 

  • Komolafe AA, Awe BS, Olorunfemi IE, Oguntunde PG (2020) Modelling flood prone area and vulnerability using integration of multi-criteria analysis and HAND model in the Ogun River Basin, Nigeria. Hydrol Sci J. https://doi.org/10.1080/02626667.2020.1764960

    Article  Google Scholar 

  • Menabde M, Seed A, Pegram G (1999) A simple scaling model for extreme rainfall. Water Resour Res 35(1):335–339

    Article  Google Scholar 

  • Mishra BK, Herath S (2014) Assessment of future floods in the Bagmati river basin of Nepal using bias-corrected daily GCM precipitation data. J Hydrol Eng 20(8):05014027

    Article  Google Scholar 

  • Nasiri H, Mohd Yusof MJ, Mohammad Ali TA (2016) An overview to flood vulnerability assessment methods. Sustain Water Resour Manag 2:331–336. https://doi.org/10.1007/s40899-016-0051-x

    Article  Google Scholar 

  • Nkwunonwo UC, Whitworth M, Baily B (2020) A review of the current status of flood modelling for urban flood risk management in the developing countries. Sci Afr 7:e00269. https://doi.org/10.1016/j.sciaf.2020.e00269

    Article  Google Scholar 

  • Nobre CA et al (2010) Vulnerabilidades das Megacidades Brasileiras às Mudanças Climáticas: região Metropolitana de São Paulo. São Paulo. Inpe; Unicamp; Usp; Ipt; Unesp- Rio Claro. https://cetesb.sp.gov.br/proclima/wp-content/uploads/sites/36/2014/05/1_megacidades.pdf

  • Nobre AD et al (2011) Height above the nearest drainage—a hydrologically relevant new terrain model. J Hydrol 404(2011):13–29. https://doi.org/10.1016/j.jhydrol.2011.03.051

    Article  Google Scholar 

  • Nobre AD et al (2015) HAND contour: a new proxy predictor of inundation extent. Hydrol Process. https://doi.org/10.1002/hyp.10581

    Article  Google Scholar 

  • Olorunfemi IE, Komolafe AA, Fasinmirin JT, Olufayo AA, Akande OS (2020) A GIS-based Assessment of the Potential Soil Erosion and Flood Hazard Zones in Ekiti State, Southwestern Nigeria using Integrated RUSLE and HAND Models. CATENA. https://doi.org/10.1016/j.catena.2020.104725

    Article  Google Scholar 

  • Pathan AI, Agnihotri PG (2021) Application of new HEC-RAS version 5 for 1D hydrodynamic flood modeling with special reference through geospatial techniques: a case of River Purna at Navsari, Gujarat, India. Model Earth Syst Environ 7:1133–1144. https://doi.org/10.1007/s40808-020-00961-

    Article  Google Scholar 

  • Petra L (2020) Tropical cyclones cause highest losses Natural disasters of 2019 in figures. https://www.munichre.com/topics-online/en/climate-change-and-natural-disasters/natural-disasters/natural-disasters-of-2019-in-figures-tropical-cyclones-cause-highest-losses.html#. Accessed 24 Jun 2020

  • Rennó CD, Nobre AD, Cuartas LA, Soares JV, Hodnett MG, Tomasella J, Waterloo MJ (2008) HAND, a new terrain descriptor using SRTM-DEM: mapping terra-firme rainforest environments in Amazônia. Remote Sens Environ 112:3469–3481

    Article  Google Scholar 

  • Tanoue M, Hirabayashi Y, Ikeuchi H (2016) Global-scale river flood vulnerability in the last 50 years. Sci Rep 6:36021. https://doi.org/10.1038/srep36021

    Article  Google Scholar 

  • Tarboton DG (1989) The analysis of river basins and channel networks using digital terrain data. Sc.D. Thesis, M.I.T., Cambridge, MA, (Also available as Tarboton D. G., R. L. Bras and I. Rodriguez-Iturbe, (Same title), Technical report no 326, Ralph M. Parsons Laboratory for Water resources and Hydrodynamics, Department of Civil Engineering, M.I.T., September 1989)

  • Teng J, Jakeman AJ, Vaze J, Croke BFW, Dutta D, Kim S (2017) Flood inundation modelling: a review of methods, recent advances and uncertainty analysis. Environ Model Softw 90:201–216. https://doi.org/10.1016/j.envsoft.2017.01.006

    Article  Google Scholar 

  • UNISDR (2013a) Global assessment report on disaster risk reduction: from shared risk to shared value: the business case for disaster risk reduction. United Nations office of Disaster Risk Reduction (UNISDR), Geneva

    Google Scholar 

  • UNISDR (2013b) Loss data and extensive/intensive risk analysis. United Nations Office of Disaster Risk Reduction (UNISDR), Geneva

    Google Scholar 

  • USDA (1986) Urban hydrology for small watersheds. United States Department of Agriculture, London

    Google Scholar 

Download references

Acknowledgements

The author appreciates the support from the Japan Foundation for the United Nations University (JFUNU). I acknowledge the support received from Prof. Herath Srikantha and Dr. Ram Avtar. Thanks to the Japan Aerospace Exploration Agency (JAXA) for the provision of satellite images.

Author information

Authors and Affiliations

  1. Institute for the Advance Study of Sustainability, United Nations University, Tokyo, Japan

    Akinola Adesuji Komolafe

  2. Department of Remote and GIS, Federal University of Technology, Akure, Nigeria

    Akinola Adesuji Komolafe

Authors
  1. Akinola Adesuji Komolafe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akinola Adesuji Komolafe.

Ethics declarations

Conflict of interest

There is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Komolafe, A.A. Integrated methodology for urban flood inundation modeling: a case study of Ichinomiya River Basin, Japan. Model. Earth Syst. Environ. 8, 2001–2010 (2022). https://doi.org/10.1007/s40808-021-01204-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-021-01204-6

Keywords

Search

Navigation